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Statistical complexity is maximized close to criticality in cortical dynamics.

Nastaran Lotfi1, Thaís Feliciano1, Leandro A A Aguiar2

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Brain activity exhibits complex dynamics, transitioning between ordered and disordered states. Researchers found maximum statistical complexity near criticality, indicating a unique intermediate brain state.

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Area of Science:

  • Neuroscience
  • Complex Systems Science
  • Information Theory

Background:

  • Complex systems exhibit dynamics between ordered and random states.
  • Brain activity shows a range of cortical states from synchronous to desynchronized.
  • Previous research indicated a phase transition in cortical states with intermediate spiking variability.

Purpose of the Study:

  • To investigate the complexity of brain signals using a symbolic information approach.
  • To identify an intermediate state of maximum complexity in cortical activity.
  • To analyze complexity in both biological (rat brain) and model systems near criticality.

Main Methods:

  • Symbolic information theory
  • Jensen disequilibrium measure
  • Analysis of cortical spiking data from urethane-anesthetized rats
  • Simulation of a network model of excitable elements

Main Results:

  • Shannon entropy increases monotonically between ordered and disordered regimes.
  • Jensen disequilibrium reveals an intermediate state of maximum complexity.
  • Statistical complexity is maximized near the critical point in both rat brain data and the network model.
  • This finding supports the existence of a critical state in brain activity.

Conclusions:

  • A distinct intermediate state of maximum complexity exists in brain activity.
  • This complex state is associated with criticality and phase transitions.
  • The findings are consistent across biological neural data and computational models.
  • Symbolic information measures, like Jensen disequilibrium, are valuable for characterizing complex neural dynamics.